Abstract

Although necessary information on prognostic implications is missing and reliable data are available in very few areas of medicine, there is an increasing demand for diagnostic decision support systems (DDSS), mainly due to the multitude of variables involved and highly complex relations between them. Unfortunately, existing approaches seem inadequate for providing accurate and high quality data – a prerequisite for establishing a successful DDSS. In this paper, we demonstrate how SISDS methodology that aims to remedy the deficiencies of current systems in use can be utilized to ease the data collection process and provide opportunities to construct DDSSs without tedious pre-processing and data preparation steps. We also provide empirical results on a real-world testbed application in the field of radiology.